This study presents a comprehensive literature review on the convergence of affective
computing, interactive installation art, multi-dimensional sensory stimulation, and artificial …

Recent technological developments have enabled computers to identify and categorize
facial expressions to determine a person's emotional state in an image or a video. This …

Accurately detecting falls and providing clear directions for the fall can greatly assist medical
staff in promptly developing rescue plans and reducing secondary injuries during …

Objectives: The temporal and spatial information of electroencephalogram (EEG) signals is
crucial for recognizing features in emotion classification models, but it excessively relies on …

Affective computing is a multidisciplinary field encompassing artificial intelligence, natural
language processing, linguistics, computer science, and social sciences. This field aims to …

Emotions expressed by humans can be identified from facial expressions, speech signals, or
physiological signals. Among them, the use of physiological signals for emotion …

In recent years, emotion recognition has received significant attention, presenting a plethora
of opportunities for application in diverse fields such as human–computer interaction …

A picture is worth a thousand words to convey information during daily life communication.
Recognizing a person's emotions from facial expressions (FEs) has become a prevalent …

In recent years, convolutional neural networks (CNNs) have played a dominant role in facial
expression recognition. While CNN-based methods have achieved remarkable success …

Part I of this chapter deals with conceptualizing emotion. By splitting up 'emotion'into an
evaluative, expressive, behavioural, physiological, mental and phenomenological …